Azure Data Engineer

Title: Azure Data Engineer

Duration: Long Term

Location: NJ (Day 1 Onsite)

Overview

This role is for an experienced Azure Data Engineer who can design, build, and support scalable data engineering solutions on Microsoft Azure. The individual will work on modern data platforms involving batch and near-real-time ingestion, data transformation, data lake and warehouse integration, and operational data workloads using Azure-native services.

The role requires strong hands-on engineering capability in PySpark, Python, SQL, Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Azure Synapse Analytics, and Azure Cosmos DB. The candidate should be able to convert business and data requirements into reliable, secure, performant, and production-ready data pipelines.

Required skills:

• 9+ years of experience in data engineering, cloud data platforms, ETL/ELT development, or large-scale data processing
• Strong hands-on experience in designing, developing, testing, and maintaining Azure-based data pipelines and data processing solutions
• Must have strong hands-on experience with PySpark and Python for large-scale data transformation, automation, data quality checks, and reusable data engineering frameworks
• Azure Data Factory for data ingestion, orchestration, parameterized pipelines, triggers, and monitoring
• Azure Databricks and Apache Spark for scalable data processing using PySpark notebooks, jobs, workflows, and optimized Spark transformations
• Azure Data Lake Storage Gen2 for lakehouse-style storage, folder structures, file formats, access control, and lifecycle management
• Azure Synapse Analytics or Azure SQL for analytical workloads, SQL development, data modeling, performance tuning, and reporting integration
• Azure Cosmos DB for NoSQL data modeling, partition key design, indexing strategy, throughput optimization, change feed processing, and integration with analytics pipelines

Required technical skills:

• Strong Python programming skills, including data structures, functions, exception handling, logging, reusable modules, API integration, and automation scripts
• Strong PySpark development experience using DataFrame APIs, joins, aggregations, window functions, UDFs, partitioning, caching, broadcast joins, and performance optimization
• Good SQL skills for querying, transformation, data validation, stored procedures, performance tuning, and troubleshooting data issues
• Experience with Git, Azure DevOps, CI/CD practices, unit testing, deployment pipelines, monitoring, and production support for data engineering workloads

Responsibilities:

Data Engineering Design & Development

Design, develop, and maintain scalable Azure data engineering solutions across:

• Batch, incremental, and near-real-time data ingestion from databases, APIs, files, applications, and streaming sources
• Azure Data Factory, Azure Databricks, ADLS Gen2, Azure Synapse Analytics, Azure SQL, and Azure Cosmos DB

Build and optimize data pipelines for:

• Data extraction, cleansing, transformation, enrichment, validation, and loading into curated data layers
• Reusable PySpark frameworks, parameterized notebooks, modular Python components, and metadata-driven processing patterns
• Data quality controls, exception handling, audit logging, reconciliation, restartability, and operational monitoring

Develop Cosmos DB-based data solutions by:

• Designing containers, partition keys, indexing policies, consistency levels, TTL, and throughput configuration based on access patterns
• Implementing ingestion and integration patterns between Cosmos DB, Azure Data Factory, Databricks, ADLS, and analytical stores
• Using Cosmos DB change feed, bulk operations, query tuning, partition-aware design, and cost optimization practices

Pipeline Delivery, Optimization & Support

Own hands-on delivery across:

• PySpark-based ETL/ELT jobs for large-scale structured, semi-structured, and unstructured data processing
• Python-based automation, data validation utilities, reusable transformation logic, and integration scripts
• Azure Data Factory pipelines, Databricks jobs, Synapse SQL workloads, Cosmos DB integrations, and downstream analytics data products

Drive engineering discipline through:

• Code reviews, unit testing, version control, CI/CD, deployment automation, and environment configuration management
• Pipeline monitoring, failure handling, performance tuning, cost optimization, and production incide…